AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective
Generative AI tools provide novice programmers with instant, personalized support, but also raise concerns about whether AI use supports or bypasses students' regulation of problem-solving. Existing work has largely focused on correctness, usability, or overall usage frequency, with less attention to how student--AI help-seeking unfolds. This study addresses this gap by analyzing AI-assisted help-seeking trajectories in university-level programming. Using an SRL-informed analytical framework that links prompt-level help-seeking codes to conceptual, implementation, debugging, and reflective forms of support, we analyzed 1,290 task-specific student prompts linked to 17,190 code submissions ...
| Authors | Boxuan Ma, Huiyong Li, Gen Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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